Machine Learning

In the era of Big Data, data has become an organization’s most valuable asset. However, access to it is often limited by a technical barrier: the need to use query languages like SQL. For years, analysts and engineers have dreamed of a system that would allow them to “talk” to a database in natural language. Text-to-SQL systems aim to realize this vision, but their path has been challenging. Older models, though promising, often failed in real-world scenarios: they were “brittle,” struggled with unseen database schemas, and required costly fine-tuning for each new domain. ...

In an era where Large Language Models (LLMs) like GPT-4 or Llama seem to understand the world, a fundamental challenge remains: how to teach them effectively and efficiently? The standard method is Supervised Fine-Tuning (SFT), which involves “feeding” the model thousands of examples of correct responses. However, as the groundbreaking paper “On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification” (arXiv:2508.05629) points out, SFT has a hidden flaw that limits its true potential. ...

In a world where robots and AI systems increasingly learn through observation and interaction with humans, the efficiency of this process remains a key challenge. Traditional Imitation Learning methods often require a human teacher to constantly supervise and correct errors, which is time-consuming and costly. A team of researchers led by Jelle Luijkx proposes a groundbreaking solution in their latest paper, “ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning.” ...

Can computers learn to “hear” the rhythm in a stream of data, much like we hear the rhythm in music? And by using this skill, can they better protect us from equipment failures, financial fraud, or health problems? A new scientific paper titled “CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series” attempts to answer these questions. The Problem with Anomalies We live in a world of data. From our heartbeats and stock market fluctuations to energy consumption in a smart city—all of this is time series data, collected at regular intervals. Often lurking within this data are anomalies: strange, unexpected events that can signal a problem. This could be a sudden cardiac arrhythmia, a suspicious bank transaction, or an impending engine failure in a factory. ...

In the world of artificial intelligence, data is the fuel that powers machine learning models. But what if that fuel is contaminated? Mislabeled data, known as label noise, is a huge problem that can cause even the best algorithms to learn complete nonsense. The paper “ε-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise,” accepted at the prestigious NeurIPS 2024 conference, offers an elegant solution. The Problem: When a Model Blindly Trusts Its Labels Let’s imagine we’re training a model to recognize animals. We show it a picture of a cute cat. In the traditional approach, we give it an absolutely certain piece of information, a so-called one-hot vector: ...

In the field of machine learning, a model’s ability to assess its own confidence is crucial for its reliability, especially in high-stakes applications like medicine or autonomous vehicles. The arXiv paper 2508.00754, titled “A Simple and Effective Method for Uncertainty Quantification and OOD Detection”, by Yaxin Ma, Benjamin Colburn, and Jose C. Principe, introduces an innovative and efficient approach to this problem. The paper focuses on two related concepts: uncertainty quantification and Out-of-Distribution (OOD) detection. ...

Clinical trial enrollment is a critical bottleneck in drug development: nearly 80% of trials fail to meet target enrollment, costing up to $8 million per day if delayed. In this work, we introduce a multimodal deep‐learning framework that not only predicts total participant count but also quantifies uncertainty around those predictions. Challenges in Enrollment Forecasting Traditional approaches fall into two camps: Deterministic models – e.g. tabular ML like XGBoost or LightGBM – which output a point estimate but ignore variability in recruitment rates. Stochastic models – e.g. Poisson or Poisson–Gamma processes – which simulate recruitment and give confidence intervals, but often struggle with high-dimensional, heterogeneous data. Model Architecture Inputs ...

The paper “Consensus-Driven Active Model Selection” introduces CODA, a method that selects the best machine learning model using the predictions of many candidate models and minimal labeled data. CODA builds a probabilistic framework that leverages model agreement and disagreement to guide which examples should be labeled next. 🚀 Key Concepts Active model selection: Instead of labeling a full validation set, CODA selectively chooses which data points to label by estimating which would be most informative. Consensus modeling: CODA uses a Bayesian adaptation of the Dawid-Skene model to evaluate model performance based on agreement among models. PBest distribution: Represents the current belief about which model is best, updated with each newly labeled data point. 🧪 How Does CODA Work? Model predictions are collected over unlabeled data. A consensus label for each data point is calculated using a weighted sum of predictions from all models. Each model is assigned a confusion matrix prior using a Dirichlet distribution: $$ \theta_{k, c, c’} = \frac{\beta_{c, c’} + \alpha \hat{M}_{k, c, c’}}{T} $$ CODA updates a probabilistic estimate over which model is best: $$ PBest(h_k) = \int_0^1 f_k(x) \prod_{l \ne k} F_l(x) dx $$ It selects the next data point to label by maximizing expected information gain: $$ EIG(x_i) = H(PBest) - \sum_c \hat{\pi}(c \mid x_i) H(PBest^c) $$ 📊 Results CODA outperforms previous state-of-the-art methods on 18 out of 26 benchmark tasks. Achieves optimal model selection with up to 70% fewer labels compared to baselines. Especially effective in multi-class tasks (e.g., DomainNet, WILDS). ❗ Limitations In binary classification with high data imbalance, CODA may underperform due to biased early estimates (e.g., CivilComments, CoLA datasets). CODA assumes that consensus is meaningful; highly divergent models may reduce effectiveness. 🔮 Future Work Better priors from human knowledge or unsupervised features. Extension to non-classification tasks and alternative metrics. Integration with active learning and active testing frameworks. Links Based on the publication 📄 arXiv:2507.23771 PDF

Ever wondered whether that expensive jar of “acacia honey” is the real deal? Or if the origin listed on the label truly reflects the soil and flowers it came from? In a new study, researchers used machine learning and mineral analysis to uncover the botanical and geographical roots of honey — all without needing a microscope. The Science Behind It When bees produce honey, they also carry tiny traces of minerals from the plants and soil around them. These mineral fingerprints — elements like calcium, magnesium, or zinc — vary depending on the environment. By measuring them, we can build a kind of chemical signature for each honey. ...

In many real-world tasks—like forecasting the paths of cars at a busy intersection, coordinating fleets of delivery robots, or simulating pedestrian movement—models must reason about not just where things are, but how they face or rotate relative to each other. That’s the SE(2) geometry: 2D position + heading. Traditional Transformer models that account for rotation and translation invariance (SE(2)-invariant) need to compute relative poses between every pair of objects. If you have $n$ objects, this leads to memory cost growing like $O(n^2)$—which becomes prohibitively expensive when $n$ is large. ...